






'''simple docstring'''





from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def __UpperCAmelCase   (					)    ->   tuple[list[int], int]:
			UpperCAmelCase_      :       Optional[Any]          =       [randint(-1_0_0_0			,       1_0_0_0		) for i in range(1_0		)]
			UpperCAmelCase_      :       int          =       randint(-5_0_0_0			,       5_0_0_0		)
			return (arr, r)


_UpperCamelCase						:						Dict  =					make_dataset()
def __UpperCAmelCase   (					A					:			list[int]			,       A					:			int		)    ->   tuple[int, ...]:
			for triplet in permutations(A			,       3		):
						if sum(A		) == target:
									return tuple(sorted(A		)		)
			return (0, 0, 0)
def __UpperCAmelCase   (					A					:			list[int]			,       A					:			int		)    ->   tuple[int, int, int]:
			arr.sort()
			UpperCAmelCase_      :       List[str]          =       len(A		)
			for i in range(n - 1		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       i + 1, n - 1
						while left < right:
									if arr[i] + arr[left] + arr[right] == target:
												return (arr[i], arr[left], arr[right])
									elif arr[i] + arr[left] + arr[right] < target:
												left += 1
									elif arr[i] + arr[left] + arr[right] > target:
												right -= 1
			return (0, 0, 0)
def __UpperCAmelCase   (					)    ->   tuple[float, float]:
			UpperCAmelCase_      :       Any          =       '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
			UpperCAmelCase_      :       str          =       '''
triplet_sum1(*dataset)
'''
			UpperCAmelCase_      :       Dict          =       '''
triplet_sum2(*dataset)
'''
			UpperCAmelCase_      :       int          =       repeat(setup=A			,       stmt=A			,       repeat=5			,       number=1_0_0_0_0		)
			UpperCAmelCase_      :       Any          =       repeat(setup=A			,       stmt=A			,       repeat=5			,       number=1_0_0_0_0		)
			return (min(A		), min(A		))


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						_UpperCamelCase						:						Optional[Any]  =					solution_times()
						print(f'''The time for naive implementation is {times[0]}.''')
						print(f'''The time for optimized implementation is {times[1]}.''')










'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Dict  =					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"mvp"
					a_        =					["past_key_values"]
					a_        =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(  self  :		Any				, _A  :		List[str]=5_02_67				, _A  :		Dict=10_24				, _A  :		List[str]=12				, _A  :		Dict=40_96				, _A  :		Optional[int]=16				, _A  :		Optional[Any]=12				, _A  :		int=40_96				, _A  :		Dict=16				, _A  :		Union[str, Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		int="gelu"				, _A  :		Union[str, Any]=10_24				, _A  :		int=0.1				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.02				, _A  :		Optional[Any]=0.0				, _A  :		str=False				, _A  :		List[Any]=True				, _A  :		Optional[int]=1				, _A  :		Tuple=0				, _A  :		Dict=2				, _A  :		Dict=True				, _A  :		int=2				, _A  :		Tuple=2				, _A  :		List[str]=False				, _A  :		Any=1_00				, _A  :		Union[str, Any]=8_00				, **_A  :		List[str]				, )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       int          =       max_position_embeddings
								UpperCAmelCase_      :       Dict          =       d_model
								UpperCAmelCase_      :       Dict          =       encoder_ffn_dim
								UpperCAmelCase_      :       Optional[Any]          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       encoder_attention_heads
								UpperCAmelCase_      :       List[Any]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[Any]          =       decoder_layers
								UpperCAmelCase_      :       Dict          =       decoder_attention_heads
								UpperCAmelCase_      :       str          =       dropout
								UpperCAmelCase_      :       Optional[Any]          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       activation_function
								UpperCAmelCase_      :       Union[str, Any]          =       init_std
								UpperCAmelCase_      :       Optional[int]          =       encoder_layerdrop
								UpperCAmelCase_      :       Optional[int]          =       decoder_layerdrop
								UpperCAmelCase_      :       Dict          =       classifier_dropout
								UpperCAmelCase_      :       List[Any]          =       use_cache
								UpperCAmelCase_      :       Dict          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase_      :       int          =       use_prompt
								UpperCAmelCase_      :       Tuple          =       prompt_length
								UpperCAmelCase_      :       str          =       prompt_mid_dim

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, is_encoder_decoder=_A				, decoder_start_token_id=_A				, forced_eos_token_id=_A				, **_A				, )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				, _A      ):
											UpperCAmelCase_      :       Optional[Any]          =       self.bos_token_id
											warnings.warn(
											    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''      )


